Computer Optics

172-178

Authors: S.S. Stafeev, V.D. Zaitsev

By numerically simulating the sharp focusing of fractional-order vector beams (0≤m≤1, with azimuthal polarization at m=1 and linear polarization at m=0), it is shown that the shape of the intensity distribution in the focal spot changes from elliptical (m=0) to round (m=0.5) and ends up being annular (m=1). Meanwhile, the distribution pattern of the longitudinal component of the Poynting vector (energy flux) in the focal spot changes in a different way: from circular (m=0) to elliptical (m=0.5) and ends up being annular (m=1). The size of the focal spot at full width at half maximum of intensity for a first-order azimuthally polarized optical vortex (m=1) and numerical aperture NA=0.95 is found to be 0.46 of the incident wavelength, whereas the diameter of the on-axis energy flux for linearly polarized light (m=0) is 0.45 of the wavelength. Therefore, the answers to the questions: when the focal spot is round and when elliptical, or when the focal spot is minimal -- when focusing an azimuthally polarized vortex beam or a linearly polarized non-vortex beam, depend on whether we are considering the intensity at the focus or the energy flow. In another run of numerical simulation, we investigate the effect of the deviation of the beam order from m=2 (when an energy backflow is observed at the focal spot center). The reverse energy flow is shown to occur at the focal spot center until the beam order gets equal to m=1.55.

190-199

Authors: V.V. Kotlyar, A.A. Kovalev, A.G. Nalimov

It is theoretically shown how a Gaussian beam with a finite number of parallel lines of intensity nulls (edge dislocations) is transformed using a cylindrical lens into a vortex beam that carries orbital angular momentum (OAM) and has a topological charge (TC). In the initial plane, this beam already carries OAM, but does not have TC, which appears as the beam propagates further in free space. Using an example of two parallel lines of intensity nulls symmetrically located relative to the origin, we show the dynamics of the formation of two intensity nulls at the double focal length: as the distance between the vertical lines of intensity nulls is being increased, two optical vortices are first formed on the horizontal axis, before converging to the origin and then diverging on the vertical axis. At any distance between the zero-intensity lines, the optical vortex has the topological charge TC=–2, which conserves at any on-axis distance, except the initial plane. When the distance between the zero-intensity lines changes, the OAM that the beam carries also changes. It can be negative, positive, and at a certain distance between the lines of intensity nulls OAM can be equal to zero. It is also shown that for an unlimited number of zero-intensity lines, a beam with finite OAM and an infinite TC is formed.

208-213

Authors: A.A. Savelyeva, E.S. Kozlova

Focusing of a linearly polarized laser beam of wavelength 633 nm with two-layer dielectric microcylinders of a circular cross-section and 2-um diameter was simulated using a finite-difference time-domain (FDTD) method, implemented using the FullWAVE software. It was shown that using a cladding whose refractive index (1.8 or 1.9) is higher than that of the core (1.45), it is possible to increase the depth of focus by a factor of 2.57 multiplied by the incident wavelength and shift the focal spot position along the optical axis away from the microcylinder boundary. It was also shown that parameters of the microcylinder could be chosen in such a way that a tighter focal spot was generated, with its full width at half maximum of intensity being 2.27 of the incident wavelength. The intensity at this focus was shown to be 1.4 times higher than that at the focus generated with a homogeneous microcylinder.

222-226

Authors: A.A. Esin, A.R. Akhmatkhanov, V.S. Pavelyev, V.Y. Shur

A diffractive optical element based on a nonlinear-optical ferroelectric single crystal has been proposed, synthesized, and experimentally characterized. The element allows fast modulation of transverse modes of a Gaussian laser beam.

235-244

Authors: A.S. Minkin, O.V. Nikolaeva, A.A. Russkov

The paper is aimed at developing an algorithm of hyperspectral data compression that combines small losses with high compression rate. The algorithm relies on a principal component analysis and a method of exhaustion. The principal components are singular vectors of an initial signal matrix, which are found by the method of exhaustion. A retrieved signal matrix is formed in parallel. The process continues until a required retrieval error is attained. The algorithm is described in detail and input and output parameters are specified. Testing is performed using AVIRIS data (Airborne Visible-Infrared Imaging Spectrometer). Three images of differently looking sky (clear sky, partly clouded sky, and overcast skies) are analyzed. For each image, testing is performed for all spectral bands and for a set of bands from which high water-vapour absorption bands are excluded. Retrieval errors versus compression rates are presented. The error formulas include the root mean square deviation, the noise-to-signal ratio, the mean structural similarity index, and the mean relative deviation. It is shown that the retrieval errors decrease by more than an order of magnitude if spectral bands with high gas absorption are disregarded. It is shown that the reason is that weak signals in the absorption bands are measured with great errors, leading to a weak dependence between the spectra in different spatial pixels. A mean cosine distance between the spectra in different spatial pixels is suggested to be used to assess the image compressibility.

253-260

Authors: I.V. Zenkov, A.V. Lapko, V.A. Lapko, S.T. Im, V.P. Tuboltsev, V.L. Аvdeenok

A nonparametric algorithm for automatic classification of large statistical data sets is proposed. The algorithm is based on a procedure for optimal discretization of the range of values of a random variable. A class is a compact group of observations of a random variable corresponding to a unimodal fragment of the probability density. The considered algorithm of automatic classification is based on the «compression» of the initial information based on the decomposition of a multidimensional space of attributes. As a result, a large statistical sample is transformed into a data array composed of the centers of multidimensional sampling intervals and the corresponding frequencies of random variables. To substantiate the optimal discretization procedure, we use the results of a study of the asymptotic properties of a kernel-type regression estimate of the probability density. An optimal number of sampling intervals for the range of values of one- and two-dimensional random variables is determined from the condition of the minimum root-mean square deviation of the regression probability density estimate. The results obtained are generalized to the discretization of the range of values of a multidimensional random variable. The optimal discretization formula contains a component that is characterized by a nonlinear functional of the probability density. An analytical dependence of the detected component on the antikurtosis coefficient of a one-dimensional random variable is established. For independent components of a multidimensional random variable, a methodology is developed for calculating estimates of the optimal number of sampling intervals for random variables and their lengths. On this basis, a nonparametric algorithm for the automatic classification is developed. It is based on a sequential procedure for checking the proximity of the centers of multidimensional sampling intervals and relationships between frequencies of the membership of the random variables from the original sample of these intervals. To further increase the computational efficiency of the proposed automatic classification algorithm, a multithreaded method of its software implementation is used. The practical significance of the developed algorithms is confirmed by the results of their application in processing remote sensing data.

267-276

Authors: N.N. Evtikhiev, A.V. Kozlov, V.V. Krasnov, V.G. Rodin, R.S. Starikov, P.A. Cheremkhin

Currently, cameras are widely used in scientific, industrial and amateur tasks. Thus, one needs to be able to quickly evaluate characteristics and capabilities of a particular camera. A method for measuring noise components of the camera photosensor is proposed. It allows one to estimate shot noise, dark temporal noise, photo response non-uniformity and dark signal non-uniformity. For noise measurement, just two images of the same scene need to be registered. The scene consists of several stripes (quasihomogeneous regions). Then the images are processed by automatic signal segmentation. The performance and accuracy of the proposed method are higher than or equal to other fast methods. The experimental results obtained are similar to those derived using a time-consuming standard method within a measurement error.

286-295

Authors: G.A. Samigulina, T.I. Samigulin

The article discusses current issues related to the development of an information optical Smart technology for distance learning of Honeywell's distributed Experion PKS control system for the oil and gas industry. About 70 % of industrial accidents are caused by the human factor through the fault of operators. The work of operators consists in monitoring and managing high-tech proc-esses through mnemonic scheme circuits and is characterized by increased tension in the visual apparatus, as well as general fatigue and loss of concentration. The innovative personalized tech-nology of distance learning takes into account the peculiarities of students' vision by adjusting the color supply of educational material and the dynamic presentation of information depending on the person's psychotype and is based on the use of cognitive, optical, multi-agent technologies, as well as ontological and immuno-network approaches. The development of cognitive mnemonic schemes is carried out taking into account these features, which allows one to reduce the load on the visual apparatus and increase the effectiveness of teaching practical skills when working with mnemonic schemes. An artificial immune systems approach is used to predict and evaluate the learning process and promptly adjust the knowledge obtaining process. A modified algorithm for the functioning of a distance learning system based on the use of optimization algorithms for arti-ficial intelligence and an algorithm for immuno-network modeling has been developed. General principles of creating mimic diagrams and existing Honeywell mnemonic schemes are considered. An example of the implementation of the proposed remote technology is presented and results of the simulation of cognitive mnemonic scheme for various categories of students with special needs are discussed.

301-305

Authors: D.M. Murashov, Y.V. Obukhov, I.A. Kershner, M.V. Sinkin

One of the problems solved by analyzing the data of long-term Video EEG monitoring is the differentiation of epileptic and artifact events. For this, not only multichannel EEG signals are used, but also video data analysis, since traditional methods based on the analysis of EEG wavelet spectrograms cannot reliably distinguish an epileptic seizure from a chewing artifact. In this paper, we propose an algorithm for detecting artifact events based on a joint analysis of the level of the optical flow and the ridges of wavelet spectrograms. The preliminary results of the analysis of real clinical data are given. The results show the possibility in principle of reliable distinguishing non-epileptic events from epileptic seizures.